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10 questions
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  • Q1
    A predictor variable can also be a dependent variable in the same regression model.
    True
    False
    30s
  • Q2
    You want to predict the price of your house based on the size of its interior and garden, both expressed in square meters. You should use:
    You can use either
    Logistic Regression
    Linear Regression
    30s
  • Q3
    What do we need to assume about the error terms in linear regression? Choose all that apply.
    They need to be independent and identically distributed
    They need to be identically distributed, but the shape of the distribution does not matter
    They need to have a normal distribution but the mean does not matter
    30s
  • Q4
    Fitting a regression model means estimating the parameters of the regression model based on the distribution of the sample data.
    True
    False
    30s
  • Q5
    We cannot use the least squares method to estimate parameters for Logistic Regression.
    False
    True
    30s
  • Q6
    Knowing that the dependent variable is normally distributed is a sufficient condition for being able to use Linear Regression.
    False
    True
    30s
  • Q7
    If a predictor variable is deemed by an anova to not be significant in a model but its interaction with another predictor variable is, we should:
    Remove the non-significant predictor variable
    Keep both variables and their interaction in, since the non-significant one is part of the model anyway
    Remove the non-significant predictor variable and its interaction with the other variable
    30s
  • Q8
    We can predict dependent variables using a regression model by filling in most (but not necessarily all) of the predictor variables.
    False
    True
    30s
  • Q9
    If we have categorical variables and we wish to use them in a linear regression, then we can do so without any other modifications, since only the dependent variable needs to be scalar.
    False
    True
    30s
  • Q10
    There can be an interaction between three or more predictor variables in a regression model.
    False
    True
    30s

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